Many strategies and approaches in the continual learning field attempt to solve the catastrophic forgetting problem in the class-incremental scenario. Regularization techniques [1]} identify essential parameters for inference of previous tasks and avoid perturbing them when learning new tasks. Knowledge distillation methods have also been used [2]}, where knowledge from previous tasks and incoming tasks are jointly optimized. Inspired by work in reinforcement learning, memory replay has also been an important direction explored by researchers [3]}, where essential knowledge acquired from previous experiences is re-used for faster training, or retraining, of a learning agent.
